Function to compute fitted values for `bamlss`

models. The function calls
`predict.bamlss`

to compute fitted values from samples.

```
# S3 method for bamlss
fitted(object, model = NULL, term = NULL,
type = c("link", "parameter"), samples = TRUE,
FUN = c95, nsamps = NULL, ...)
```

## Arguments

- object
An object of class `"bamlss"`

- model
Character or integer, specifies the model for which fitted values should be computed.

- term
Character or integer, specifies the model terms for which fitted values are required.
Note that if `samples = TRUE`

, e.g., `term = c("s(x1)", "x2")`

will compute the
combined fitted values `s(x1) + x2`

.

- type
If `type = "link"`

the predictor of the corresponding `model`

is returned. If `type = "parameter"`

fitted values on the distributional parameter scale
are returned.

- samples
Should fitted values be computed using samples of parameters or estimated parameters
as returned from optimizer functions (e.g., function `bfit`

returns
`"fitted.values"`

). The former results in a call to `predict.bamlss`

, the
latter simply extracts the `"fitted.values"`

of the `bamlss`

object and
is not model term specific.

- FUN
A function that should be applied on the samples of predictors or
parameters, depending on argument `type`

.

- nsamps
If the fitted `bamlss`

object contains samples of parameters,
computing fitted values may take quite some time. Therefore, to get a first feeling it can
be useful to compute fitted values only based on `nsamps`

samples, i.e., `nsamps`

specifies the number of samples which are extracted on equidistant intervals.

- ...
Arguments passed to function `predict.bamlss`

.

## Value

Depending on arguments `model`

, `FUN`

and the structure of the `bamlss`

model, a list of fitted values or simple vectors or matrices of fitted values.

## Examples

```
if (FALSE) ## Generate some data.
d <- GAMart()
## Model formula.
f <- list(
num ~ s(x1) + s(x2) + s(x3) + te(lon,lat),
sigma ~ s(x1) + s(x2) + s(x3) + te(lon,lat)
)
## Estimate model.
b <- bamlss(f, data = d)
#> Error in eval(expr, envir, enclos): object 'd' not found
## Fitted values returned from optimizer.
f1 <- fitted(b, model = "mu", samples = FALSE)
#> Error in eval(expr, envir, enclos): object 'b' not found
## Fitted values returned from sampler.
f2 <- fitted(b, model = "mu", samples = TRUE, FUN = mean)
#> Error in eval(expr, envir, enclos): object 'b' not found
plot(f1, f2)
#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f1' not found
```